*K-means and cluster models for cancer signatures

Kakushadze, Zura; Yu, Willie

doi:10.1016/j.bdq.2017.07.001

Cited by 31 publications

(7 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Molecular subtyping is considered as a favorable source of disease stratification ( Guinney et al, 2015 ; Sjodahl et al, 2017 ), while similar lncRNA-based subtyping is still lacking. With a k-means clustering analysis ( Kakushadze and Yu, 2017 ), we could divide 111 ESCC patients from TCGA into two molecular subgroups based on 50 lncRNA markers, and its performance was again confirmed in internal and external datasets. The lncRNA-based subtyping showed good clustering capability and could be treated as a potential tool for ESCC molecular subtyping.…”

Section: Discussionmentioning

confidence: 89%

lncRNA Profiles Enable Prognosis Prediction and Subtyping for Esophageal Squamous Cell Carcinoma

Zhang

Gao

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

Long non-coding RNAs (lncRNAs) have emerged as useful prognostic markers in many tumors. In this study, we investigated the potential application of lncRNA markers for the prognostic prediction of esophageal squamous cell carcinoma (ESCC). We identified ESCC-associated lncRNAs by comparing ESCC tissues with normal tissues. Subsequently, Kaplan–Meier (KM) method in combination with the univariate Cox proportional hazards regression (UniCox) method was used to screen prognostic lncRNAs. By combining the differential and prognostic lncRNAs, we developed a prognostic model using cox stepwise regression analysis. The obtained prognostic prediction model could effectively predict the 3- and 5-year prognosis and survival of ESCC patients by time-dependent receiver operating characteristic (ROC) curves (area under curve = 0.87 and 0.89, respectively). Besides, a lncRNA-based classification of ESCC was generated using k-mean clustering method and we obtained two clusters of ESCC patients with association with race and Barrett’s esophagus (BE) (both P < 0.001). Finally, we found that lncRNA AC007128.1 was upregulated in both ESCC cells and tissues and associated with poor prognosis of ESCC patients. Furthermore, AC007128.1 could promote epithelial-mesenchymal transition (EMT) of ESCC cells by increasing the activation of MAPK/ERK and MAPK/p38 signaling pathways. Collectively, our findings indicated the potentials of lncRNA markers in the prognosis, molecular subtyping, and EMT of ESCC.

show abstract

Section: Discussionmentioning

confidence: 89%

lncRNA Profiles Enable Prognosis Prediction and Subtyping for Esophageal Squamous Cell Carcinoma

Zhang

Gao

et al. 2021

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

show abstract

“…Some of these applications are already being documented in the literature, as was done by Salma, (2016) that used a variation of K-means (fast K-means) to select the most relevant resources from a high-dimension breast cancer data set, reaching an accuracy 99.39%. In this context, it is also worth mentioning the study of Kakushadze and Yu, (2017), in which they used 1389 published samples of 14 types of cancer and found that 3 types of cancer (liver cancer, lung cancer and renal cell carcinoma) stand out from the others and had no similar structures to the cluster. In our study, using this same algorithm, we identified that two groups have the best explanatory capacity for our data, dividing them between patients and controls with a hit rate that reached 72.81% when analyzed using the t-sne.…”

Section: Discussionmentioning

confidence: 99%

Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques

Medeiros

Lins

Samuel

et al. 2021

RSD

View full text Add to dashboard Cite

Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. Results: At first, an unsupervised classification was performed, which identified as two the number of groups that best represent the data present in our database, reaching 72.81% accuracy in the separation of patients and controls. Then, the supervised classification was performed, where the classifier obtained a 70.97% success rate, being possible to reach 75% accuracy in the best GridSearch configuration when performing a cross validation. Conclusion: It was not yet possible to conclude about the participation of the evaluated polymorphisms in the development of the BL, however the computational techniques used proved to be very promising for carrying out studies of this nature.

show abstract

“…The increasing availability of cell line data has allowed for widespread drug sensitivity prediction based on genetic profiles, and genomic data have played a key part in this effort. Pan cancer analysis and more specific interactions, such as the response to leucovorin, fluorouracil, and oxaliplatin in patients with colorectal cancer, have both made use of genomic information to predict clinical response measures [21,37,38].…”

Section: Predicting and Evaluating Treatment Responsementioning

confidence: 99%

The Evolution and Reliability of Machine Learning Techniques for Oncology

Owida¹,

Moh’d²,

Turab

et al. 2023

Int. J. Onl. Eng.

View full text Add to dashboard Cite

It is no secret that the rise of the Internet and other digital technologies has sparked renewed interest in AI-based techniques, especially those that fall under the umbrella of the subset of algorithms known as "Machine Learning" (ML). These advancements in electronics have allowed us to comprehend the world beyond the bounds of human cognition. A high-dimensional dataset's complicated nature. Although these techniques have been regularly employed by the medical sciences, their adoption to enhance patient care has been a bit slow. The availability of curated diverse data sets for model development is all examples of the substantial hurdles that have delayed these efforts. The future clinical acceptance of each of these characteristics may be affected by a number of limiting conditions, such as the time and resources spent on data collection and model development, the cost of integration relative to the time and resources spent on translation, and the potential for patient damage. In order to preserve value and enhance medical care, the goal of this article is to evaluate all facets of the issue in light of the validity of using ML methods in cancer, to serve as a template for further research and the subfield of oncology that serves as a model for other parts of the discipline.

show abstract

*K-means and cluster models for cancer signatures

Cited by 31 publications

References 71 publications

lncRNA Profiles Enable Prognosis Prediction and Subtyping for Esophageal Squamous Cell Carcinoma

lncRNA Profiles Enable Prognosis Prediction and Subtyping for Esophageal Squamous Cell Carcinoma

Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques

The Evolution and Reliability of Machine Learning Techniques for Oncology

Contact Info

Product

Resources

About